RDF Data Management

图数据在语义网、社会网络分析、生物信息学和物理通信网络等许多应用中都具有越来越重要的作用。图自然地对这些领域的复杂结构进行建模，例如社交网络或蛋白质-蛋白质相互作用网络中人与人之间的关系。这些图形数据的大小和复杂性带来了巨大的数据管理和数据分析挑战。我广泛的研究范围是对这些问题的研究。在这项发现奖励中，我的重点是从Web资源的模型中产生的图结构。资源描述框架(RDF)是通常用于对Web对象进行建模的标准(由W3C提出)。RDF是一种适用于机器理解和解释的自描述性数据模型，因此有望促进“语义网”。W3C还定义了一种称为SPARQL的查询语言，用于访问RDF存储库。RDF数据集已经开始激增和增长。例如，Yago和DBpedia从维基百科提取事实并以RDF格式存储，以方便在维基百科上进行结构化查询；许多地方政府现在正在将它们以RDF格式提供给公民的资源编码为电子政务计划的一部分；生物学家为社区共享实验数据建立了复杂的RDF数据集(BioRDF和UniProt RDF)；以及链接开放数据(LOD)计划一直在增长(截至2011年9月-这是最新的可用信息-超过3100万个三元组[元组])，作为一个网络数据集成平台。因此，管理和分析大型分布式RDF数据集成为一个紧迫而重要的问题。我的团队对RDF数据管理和分析的方法不同于现有的许多方法，这些方法以这样或那样的方式将RDF映射到关系表示形式，并将SPARQL查询转换为SQL。尽管这具有利用成熟技术的优势，但它会引起性能和建模不匹配的问题。我们将RDF数据集建模为图(这是RDF的本机模型)，并将SPARQL查询表示为图。因此，查询执行简化为图匹配。这种方法具有建模和性能优势。在这一总体方法中，我打算在未来五年内研究以下问题：1.RDF图形的高效存储结构。高效有效的查询处理和SPARQL查询优化技术(包括现在是SPARQL标准一部分的聚合查询)。分布式RDF存储上RDF图的分布和SPARQL查询的评估。基于RDF的Web数据查询和集成，需要对RDF数据进行一定的推理能力(即OWL2推理机制)。接下来的方法论包括算法研究、原型系统的开发和大量的实验。这项研究的成功完成将为RDF数据管理和Web数据集成与查询开发出高效和有效的技术。由于RDF技术现在得到了广泛的部署(包括一些国家的各级政府)，其结果将在技术和社会上产生重大影响。

Graph data are of growing importance in many applications including the semantic web, social network analysis, bioinformatics, and physical communication networks. Graphs naturally model complicated structures in these fields, such as the relationships among people in a social network or the protein-protein interaction networks. The size and complexity of these graph data raise significant data management and data analysis challenges. My broad research scope is the study of these problems.In this discovery grant, my focus is on the graph structures that arise from models of Web resources. The Resource Description Framework (RDF) is the standard (proposed by W3C) by which Web objects are commonly modeled. RDF is a self-descriptive data model that is suitable for machine understanding and interpretation, and, therefore, expected to facilitate the “semantic web”. W3C has also defined a query language, called SPARQL, for accessing RDF repositories. RDF data sets have started to proliferate and grow. For example, Yago and DBPedia extract facts from Wikipedia and store them in RDF format to facilitate structural queries over Wikipedia; many local governments are now encoding the resources they provide to citizens in RDF format as part of the e-government initiatives; biologists have built elaborate RDF data collections (BioRDF and Uniprot RDF) for community sharing of experimental data; and Linked Open Data (LOD) initiative has been growing (as of September 2011 - which are the latest available information - over 31 million triples [tuples]) as a web data integration platform. Consequently, managing and analyzing large and distributed RDF datasets have emerged as an urgent and important concern.My group’s approach to RDF data management and analysis differs from many of the existing approaches that map, in one way or another, RDF into a relational representation and convert SPARQL queries into SQL. Although this has the advantage of leveraging mature technology, it gives rise to performance and modeling mismatch problems. We model an RDF dataset as a graph (which is the native model for RDF) and also represent a SPARQL query as a graph. Consequently, query execution reduces to graph matching. This approach has modeling and performance advantages. Within this general approach, I intend to study the following issues over the next five years:1. Efficient storage structures for RDF graphs.2. Efficient and effective query processing and optimization techniques for SPARQL queries (including aggregation queries that are now part of the SPARQL standard).3. Distribution of RDF graphs and evaluation of SPARQL queries over distributed RDF stores.4. Web data querying and integration using RDF, which requires some reasoning capability over RDF data (so called OWL 2 entailment regime).The methodology that will be followed includes algorithmic studies, development of prototype systems, and extensive experimentation.Successful completion of this research will result in the development of efficient and effective techniques for RDF data management, and web data integration and querying through RDF. Since RDF technology is now widely deployed (including by various levels of government at a number of countries), the results will have significant impact both technically and societally.